I’m a data scientist by trade so I understand how “noisy” liquid asset returns are.
One of the primary rules in using a data driven approach to “forecast” future performance is to try to predict expected standard deviation before anything else.
Whether a 4 factor model, historical standard deviation, a “GARCH” model (linear regression with fluctuating standard deviation over time), or looking at the implied risk from options, I understand expected risk to be fairly easy to forecast.
I also understand forecasting expected returns to be a mathematically impossible task beyond the likes of historical performance (which sucks in quality), equity research (super expensive) or CAPM (derived from risk). A fundamental rule of quantitative finance is that there is no significant autocorrelation among any liquid assets returns across any regular time interval. As such, complex models such as neural networks (LSTMs specifically) are never used for this, but could easily pick up technical/chart “patterns” if they really even existed.
So here are my questions.
Do you believe there is any use for this form of forecasting? If not, why are there seminars, courses, sites, communities, and entire companies built around a virtually disproven form of analytics?
Submitted November 25, 2019 at 10:15PM by satakeyama https://ift.tt/2Dfpdk3
